@article{AyanuConradJentschetal.2015,
  author    = {Ayanu, Yohannes and Conrad, Christopher and Jentsch, Anke and Koellner, Thomas},
  title     = {Unveiling undercover cropland inside forests using landscape variables: a supplement to remote sensing image classification},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {6},
  doi       = {10.1371/journal.pone.0130079},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151686},
  pages     = {e0130079},
  year      = {2015},
  abstract  = {The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.},
  language  = {en}
}
@article{LauterbachBorrmannHessetal.2015,
  author    = {Lauterbach, Helge A. and Borrmann, Dorit and Heß, Robin and Eck, Daniel and Schilling, Klaus and N{\"u}chter, Andreas},
  title     = {Evaluation of a Backpack-Mounted 3D Mobile Scanning System},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  number    = {10},
  doi       = {10.3390/rs71013753},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126247},
  pages     = {13753-13781},
  year      = {2015},
  abstract  = {Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.},
  language  = {en}
}
@article{WalzWegmannLeutneretal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Leutner, Benjamin and Dech, Stefan and Vounatsou, Penelope and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling},
  series = {Geospatial Health},
  volume    = {10},
  journal   = {Geospatial Health},
  number    = {2},
  doi       = {10.4081/gh.2015.398},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126148},
  pages     = {398},
  year      = {2015},
  abstract  = {Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in C{\^o}te d'Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70\% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.},
  language  = {en}
}
@article{WalzWegmannDechetal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Vounastou, Penelope and Poda, Jean-Noel and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing},
  series = {PLoS Neglected Tropical Diseases},
  volume    = {9},
  journal   = {PLoS Neglected Tropical Diseases},
  number    = {11},
  doi       = {10.1371/journal.pntd.0004217},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125845},
  pages     = {e0004217},
  year      = {2015},
  abstract  = {Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in C{\^o}te d'Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of C{\^o}te d'Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.},
  language  = {en}
}
@article{ZoungranaConradAmekudzietal.2015,
  author    = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian},
  title     = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  number    = {9},
  doi       = {10.3390/rs70912076},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866},
  pages     = {12076-12102},
  year      = {2015},
  abstract  = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.},
  language  = {en}
}
@article{TimmermansvanderTolTimmermansetal.2015,
  author    = {Timmermans, Wim J. and van der Tol, Christiaan and Timmermans, Joris and Ucer, Murat and Chen, Xuelong and Alonso, Luis and Moreno, Jose and Carrara, Arnaud and Lopez, Ramon and Fernando de la Cruz, Tercero and Corcoles, Horacio L. and de Miguel, Eduardo and Sanchez, Jose A. G. and Perez, Irene and Belen, Perez and Munoz, Juan-Carlos J. and Skokovic, Drazen and Sobrino, Jose and Soria, Guillem and MacArthur, Alasdair and Vescovo, Loris and Reusen, Ils and Andreu, Ana and Burkart, Andreas and Cilia, Chiara and Contreras, Sergio and Corbari, Chiara and Calleja, Javier F. and Guzinski, Radoslaw and Hellmann, Christine and Herrmann, Ittai and Kerr, Gregoire and Lazar, Adina-Laura and Leutner, Benjamin and Mendiguren, Gorka and Nasilowska, Sylwia and Nieto, Hector and Pachego-Labrador, Javier and Pulanekar, Survana and Raj, Rahul and Schikling, Anke and Siegmann, Bastian and von Bueren, Stefanie and Su, Zhongbo (Bob)},
  title     = {An Overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign},
  series = {Acta Geophysica},
  volume    = {63},
  journal   = {Acta Geophysica},
  number    = {6},
  doi       = {10.2478/s11600-014-0254-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136491},
  pages     = {1465-1484},
  year      = {2015},
  abstract  = {The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.},
  language  = {en}
}
@article{NguyenKerstenSenmaoetal.2015,
  author    = {Nguyen, Duy Ba and Kersten, Clauss and Senmao, Cao and Vahid, Naeimi and Kuenzer, Claudia and Wagner, Wolfgang},
  title     = {Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  number    = {12},
  doi       = {10.3390/rs71215808},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-137554},
  pages     = {15868-15893},
  year      = {2015},
  abstract  = {Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3\% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.},
  language  = {en}
}
@article{FaOliveroRealetal.2015,
  author    = {Fa, John E. and Olivero, Jes{\´u}s and Real, Raimundo and Farf{\´a}n, Miguel A. and M{\´a}rquez, Ana L. and Vargas, J. Mario and Ziegler, Stefan and Wegmann, Martin and Brown, David and Margetts, Barrie and Nasi, Robert},
  title     = {Disentangling the relative effects of bushmeat availability on human nutrition in central Africa},
  series = {Scientific Reports},
  volume    = {5},
  journal   = {Scientific Reports},
  number    = {8168},
  doi       = {10.1038/srep08168},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144110},
  year      = {2015},
  abstract  = {We studied links between human malnutrition and wild meat availability within the Rainforest Biotic Zone in central Africa. We distinguished two distinct hunted mammalian diversity distributions, one in the rainforest areas (Deep Rainforest Diversity, DRD) containing taxa of lower hunting sustainability, the other in the northern rainforest-savanna mosaic, with species of greater hunting potential (Marginal Rainforest Diversity, MRD). Wild meat availability, assessed by standing crop mammalian biomass, was greater in MRD than in DRD areas. Predicted bushmeat extraction was also higher in MRD areas. Despite this, stunting of children, a measure of human malnutrition, was greater in MRD areas. Structural equation modeling identified that, in MRD areas, mammal diversity fell away from urban areas, but proximity to these positively influenced higher stunting incidence. In DRD areas, remoteness and distance from dense human settlements and infrastructures explained lower stunting levels. Moreover, stunting was higher away from protected areas. Our results suggest that in MRD areas, forest wildlife rational use for better human nutrition is possible. By contrast, the relatively low human populations in DRD areas currently offer abundant opportunities for the continued protection of more vulnerable mammals and allow dietary needs of local populations to be met.},
  language  = {en}
}
@article{WalzWegmannDechetal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook},
  series = {Parasites \& Vectors},
  volume    = {8},
  journal   = {Parasites \& Vectors},
  number    = {163},
  doi       = {10.1186/s13071-015-0732-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148778},
  year      = {2015},
  abstract  = {Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.},
  language  = {en}
}
@article{KuenzerKleinUllmannetal.2015,
  author    = {Kuenzer, Claudia and Klein, Igor and Ullmann, Tobias and Georgiou, Efi Foufoula and Baumhauer, Roland and Dech, Stefan},
  title     = {Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  doi       = {10.3390/rs70708516},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151552},
  pages     = {8516 -- 8542},
  year      = {2015},
  abstract  = {River deltas belong to the most densely settled places on earth. Although they only account for 5\% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta's general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas-namely the Yellow River Delta (China), the Mekong Delta (Vietnam), the Irrawaddy Delta (Myanmar), and the Ganges-Brahmaputra (Bangladesh, India)-as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013). A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid-latitude, subtropical, and polar deltas are illustrated, and the advantages and limitations of the approach for inundation derivation are discussed.},
  language  = {en}
}